AI_NLP_Series_Analysis_Application

This project demonstrates how advanced NLP techniques and LLMs can be used to analyze and interact with a popular series. By combining dataset collection, machine learning, and interactive tools, it creates an engaging way to explore a series like never before.

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Project Overview

The project is divided into five main components:

1. Dataset Collection and Web Scraping

   • Goal: Collect datasets from the web, including subtitles, transcripts, and fight classifications from the Naruto series.
   • Tools Used: Scrapy crawlers for efficient and scalable web scraping.
   • Outcome: Cleaned and structured datasets ready for analysis.

2. Character Chatbot

   • Goal: Simulate conversations with Naruto using a fine-tuned Llama LLM (8B).
   • Tools Used: Llama LLM, Hugging Face Transformers (trained with mized precision and LoRA).
   • Outcome: An interactive chatbot providing an immersive character interaction experience.

3. Character Network Visualization

   • Goal: Visualize character interactions and communication strength.
   • Tools Used: SpaCy NER, NetworkX, PyViz.
   • Outcome: A graph network revealing interaction patterns among characters.

4. Theme and Text Classifiers

   • Theme Classifier: Users input themes like "love" or "friendship" to visualize their prevalence in the series.
   • Text Classifier: Classify fight scenes based on provided descriptions.
   • Tools Used: Hugging Face Zero-Shot Classifier, Custom LLM Text Classifier.
   • Outcome: Bar plots of theme prevalence and fight scene classifications.

5. Interactive Web GUI

   • Goal: Provide users an intuitive interface to access all features.
   • Tools Used: Gradio.
   • Outcome: A seamless user experience to explore the project.

Contributing

Feel free to contribute by submitting pull requests or issues to improve the project.